Title: A New Non-Parametric Matching Method for Bias Adjustment with Applications to Economic Evaluations Authors: Jasjeet Sekhon Entrydate: 2008-05-11 23:56:50 Keywords: semiparametric and nonparametric matching methods, observational studies, randomized controlled trials, health economic evaluation Abstract: In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression and propensity score matching are model dependent and often fail to replicate the results of randomized controlled trials. We demonstrate a new non-parametric matching method, Genetic Matching, which is a generalization of propensity score and Mahalanobis distance matching, using two contrasting case studies. In the first, an economic evaluation of a clinical intervention (Pulmonary Artery Catheterization), applying Genetic Matching to observational data replicates the substantive results of a corresponding randomized controlled trial unlike the extant literature. And in the second case study evaluating capitation versus fee-for service, Genetic Matching radically improves balance on baseline covariates and overturns previous conclusions based on traditional methods. http://polmeth.wustl.edu/retrieve.php?id=745 ********************************************************** Political Methodology E-Mail List Editors: Melanie Goodrich, <[log in to unmask]> Delia Bailey, <[log in to unmask]> ********************************************************** Send messages to [log in to unmask] To join the list, cancel your subscription, or modify your subscription settings visit: http://polmeth.wustl.edu/polmeth.php **********************************************************